statsmodels.tsa.arima_model.ARIMAResults.predict ARIMAResults.predict(start=None, end=None, exog=None, typ='linear', dynamic=False) [source] ARIMA model in-sample and out-of-sample prediction Parameters: start : int, str, or datetime Zero-indexed observation number at which to start forecasting, ie., the first forecast is start. Can also be a date string to parse or a datetime type. end : int, str, or datetime Zero-indexed observation number at which to end forecasting, ie., the first fo

statsmodels.tsa.arima_model.ARIMAResults.plot_predict ARIMAResults.plot_predict(start=None, end=None, exog=None, dynamic=False, alpha=0.05, plot_insample=True, ax=None) [source] Plot forecasts Parameters: start : int, str, or datetime Zero-indexed observation number at which to start forecasting, ie., the first forecast is start. Can also be a date string to parse or a datetime type. end : int, str, or datetime Zero-indexed observation number at which to end forecasting, ie., the first f

statsmodels.tsa.arima_model.ARIMAResults.remove_data ARIMAResults.remove_data() remove data arrays, all nobs arrays from result and model This reduces the size of the instance, so it can be pickled with less memory. Currently tested for use with predict from an unpickled results and model instance. Warning Since data and some intermediate results have been removed calculating new statistics that require them will raise exceptions. The exception will occur the first time an attribute is acce

statsmodels.tsa.arima_model.ARIMAResults.load classmethod ARIMAResults.load(fname) load a pickle, (class method) Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. Returns: unpickled instance :

statsmodels.tsa.arima_model.ARIMAResults.normalized_cov_params ARIMAResults.normalized_cov_params()

statsmodels.tsa.arima_model.ARIMAResults.initialize ARIMAResults.initialize(model, params, **kwd)

statsmodels.tsa.arima_model.ARIMAResults.forecast ARIMAResults.forecast(steps=1, exog=None, alpha=0.05) [source] Out-of-sample forecasts Parameters: steps : int The number of out of sample forecasts from the end of the sample. exog : array If the model is an ARIMAX, you must provide out of sample values for the exogenous variables. This should not include the constant. alpha : float The confidence intervals for the forecasts are (1 - alpha) % Returns: forecast : array Array of out

statsmodels.tsa.arima_model.ARIMAResults.f_test ARIMAResults.f_test(r_matrix, cov_p=None, scale=1.0, invcov=None) Compute the F-test for a joint linear hypothesis. This is a special case of wald_test that always uses the F distribution. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be giv

statsmodels.tsa.arima_model.ARIMAResults.conf_int ARIMAResults.conf_int(alpha=0.05, cols=None, method='default') Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The significance level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return method : string Not Implemented Yet Method to estimate the confidence_interval. ?Defaul

statsmodels.tsa.arima_model.ARIMA.score ARIMA.score(params) Compute the score function at params. Notes This is a numerical approximation.